Numerical investigation on the heat transfer performance and optimisation of a finned heat pipe using artificial neural networks and genetic algorithm

Abstract

In this work, a lumped parameter model has been developed to study the thermal performance of a two-phase closed thermosyphon. The working fluids considered for the numerical study are water and R134a. The dimensions of the thermosyphon are, length 500 mm, ID (Inner diameter) 20 mm, and OD (outer diameter) 26 mm. The thermosyphon consists of three sections, namely, source (evaporator), adiabatic and sink (condenser), and their lengths are 200, 100 and 200 mm, respectively. The cross-sectional dimensions of fins are 5 mm × 1 mm and the length is 200 mm. Fins are placed internally at the condenser section. The mass of fluid to be charged into the thermosyphon with respect to evaporator volume, is known as the filling ratio of a thermosyphon, has been varied in this study. In the numerical model the fill ratio is varied between 20% and 80%, and fins are varied between 0 and 8. Finally, based on the parametric studies with the model an optimisation studies conducted with a combined Artificial Neural Network (ANN) and Genetic Algorithm (GA) approach to obtain the optimum conditions namely fill ratio and number of fins, at each heat input, at which the overall thermal resistance is minimum.

Keywords:

• Thermosyphon
• refrigerant
• optimisation
• ANN-GA
• thermal resistance

Disclosure statement

No potential conflict of interest was reported by the author(s).